CertNexus AIP-210 Exam - Topic 2 Question 27 Discussion

Actual exam question for CertNexus's AIP-210 exam

Question #: 27
Topic #: 2

[All AIP-210 Questions]

Which two encodes can be used to transform categories data into numerical features? (Select two.)

ACount Encoder

BLog Encoder

CMean Encoder

DMedian Encoder

EOne-Hot Encoder

Show Suggested Answer

Suggested Answer: B

A support-vector machine (SVM) is a supervised learning algorithm that can be used for classification or regression problems. An SVM tries to find an optimal hyperplane that separates the data into different categories or classes. However, sometimes the data is not linearly separable, meaning there is no straight line or plane that can separate them. In such cases, a polynomial kernel can help improve the prediction of the SVM by transforming the data into a higher-dimensional space where it becomes linearly separable. A polynomial kernel is a function that computes the similarity between two data points using a polynomial function of their features.

by Gladys at Sep 11, 2024, 01:04 AM

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Alfreda

10 months ago

One-Hot is a must, but I’m not sure about Count.

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Lizette

10 months ago

Definitely Count and Mean Encoders!

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Tamar

11 months ago

Wait, is Log Encoder even a thing? I’ve never heard of it.

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Kimbery

11 months ago

I thought Mean Encoder was also a common one?

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Lyda

11 months ago

Count Encoder and One-Hot Encoder are solid choices!

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Kenneth

11 months ago

I’m a bit confused about Log Encoder; I don’t think it’s commonly used for this purpose, but I could be wrong.

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Kimberlie

12 months ago

I feel like I've seen a question like this before, and I think Count Encoder and One-Hot Encoder were the answers.

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Cassandra

12 months ago

I think One-Hot Encoder is definitely one of the options, but I can't recall if Mean Encoder is the other one.

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Glory

12 months ago

I remember practicing with encoders, but I'm not sure if Count Encoder is one of the right answers.

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Elena

12 months ago

I'm a little confused on this one. I know One-Hot Encoder is a popular choice, but the other options like Log Encoder and Median Encoder are throwing me off. I'll have to review my notes on feature engineering techniques before answering this.

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Jacquline

12 months ago

Okay, let me think this through step-by-step. One-Hot Encoder is definitely one of the options, as it creates a binary column for each unique category. And I believe Count Encoder, which assigns a numerical value based on the count of each category, is the other correct answer. I'm feeling confident about those two.

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Kathryn

12 months ago

Hmm, I'm a bit unsure about this one. I know One-Hot Encoder is a common choice, but I can't remember the other option. Maybe Mean Encoder or Median Encoder? I'll have to think this through carefully.

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Sage

1 year ago

I'm pretty sure Count Encoder and One-Hot Encoder are the two correct options here. Those are the most common ways to transform categorical data into numerical features.

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Kiley

1 year ago

I think I've got a good handle on this. Automated Change Management is designed to handle things like splitting sales orders and updating item attributes automatically. Those seem to be the two key functions described in the options here. I'll mark those down as my answer.

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Filiberto

1 year ago

I remember we discussed similar questions in class, and the defect management process often comes up as a key area for improvement in retrospectives.

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Alesia

1 year ago

One-Hot Encoder and Median Encoder, definitely. I mean, who would even use a Log Encoder for categorical data? That's just silly.

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Laila

1 year ago

Hmm, this is a tough one. I'm leaning towards One-Hot Encoder and Median Encoder. They both seem like reasonable choices, but I'm not 100% sure.

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Lamonica

1 year ago

I'm going with One-Hot Encoder and Count Encoder. They're both great for encoding categorical data, and I'm pretty sure they're the two options we need to select here.

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Herman

1 year ago

I think the correct answers are One-Hot Encoder and Mean Encoder. They're the most commonly used techniques for transforming categorical data into numerical features.

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